Pub Date : 2025-08-06DOI: 10.1016/j.apr.2025.102681
Natasha Arina , Norfazrin Mohd Hanif , Yet Yin Hee , Afiqah Bahirah Ayoub , Siti Nursarah Mohd Rodzi , Shik Ahmad Haikal , Yasmin Zakaria , Mohd Talib Latif
Microplastic (MP) pollution poses a significant environmental threat, particularly as it enters both the atmosphere and the oceans, especially in coastal environments. This is the first study to determine the concentration of MPs in two environmental compartments, the atmosphere and the marine environment on the west coast of Peninsular Malaysia (Port Dickson and Port Klang), across different monsoon seasons. The MPs were extracted using KOH digestion and density separation using ZnCl2. The quantification of the particles was performed using Nile Red Solution. The results showed the concentrations of MPs in the atmosphere and seawater ranged from 7.16 to 7.62 particles m−3 and 156.24 to 221.90 particles L−1, respectively. A significant difference in MP concentrations between sites was observed only in seawater samples, with Port Klang exhibiting higher values. Additionally, airborne samples exhibited significant seasonal variation, with higher MP concentrations recorded during the Northeast monsoon. The observed MPs morphology consisted mostly of fragments (up to 60 %) and fibres (up to 52 %) in the airborne and marine samples, respectively. Additionally, differences in rainfall precipitation and temperature across monsoon seasons significantly impact MP concentrations in marine and airborne samples. The findings highlight the importance of monsoons and coastal activities in influencing MP inputs into the coastal environment, which provides critical insights into MP pollution in the coastal environment.
{"title":"The influence of different seasonal monsoon seasons on airborne and marine microplastics at selected stations on the west coast of Peninsular Malaysia","authors":"Natasha Arina , Norfazrin Mohd Hanif , Yet Yin Hee , Afiqah Bahirah Ayoub , Siti Nursarah Mohd Rodzi , Shik Ahmad Haikal , Yasmin Zakaria , Mohd Talib Latif","doi":"10.1016/j.apr.2025.102681","DOIUrl":"10.1016/j.apr.2025.102681","url":null,"abstract":"<div><div>Microplastic (MP) pollution poses a significant environmental threat, particularly as it enters both the atmosphere and the oceans, especially in coastal environments. This is the first study to determine the concentration of MPs in two environmental compartments, the atmosphere and the marine environment on the west coast of Peninsular Malaysia (Port Dickson and Port Klang), across different monsoon seasons. The MPs were extracted using KOH digestion and density separation using ZnCl<sub>2</sub>. The quantification of the particles was performed using Nile Red Solution. The results showed the concentrations of MPs in the atmosphere and seawater ranged from 7.16 to 7.62 particles m<sup>−3</sup> and 156.24 to 221.90 particles L<sup>−1</sup>, respectively. A significant difference in MP concentrations between sites was observed only in seawater samples, with Port Klang exhibiting higher values. Additionally, airborne samples exhibited significant seasonal variation, with higher MP concentrations recorded during the Northeast monsoon. The observed MPs morphology consisted mostly of fragments (up to 60 %) and fibres (up to 52 %) in the airborne and marine samples, respectively. Additionally, differences in rainfall precipitation and temperature across monsoon seasons significantly impact MP concentrations in marine and airborne samples. The findings highlight the importance of monsoons and coastal activities in influencing MP inputs into the coastal environment, which provides critical insights into MP pollution in the coastal environment.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102681"},"PeriodicalIF":3.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-06DOI: 10.1016/j.apr.2025.102683
Doris Haas , Theresa Fritz , Herbert Galler , Juliana Habib , Petra Ofner-Kopeinig , Michael Kropsch , Eduard Zentner , Michael Schalli
Today, horses assist us in sports and are partners in our leisure activities. The frequent consumption of roughage and bedding materials, horses and staff are often exposed to high levels of microorganisms in the stable air. The objective of the study was to investigate the emission of particulate matter and culturable fungi in two horse stables of different construction types in urban and rural areas as well as their transmission in the immediate surroundings. The emission measurements were done in an open and a closed stable. To assess the immissions, measurements were conducted at the stables entrance, leeward and windward directions. Samples were carried out using the MAS 100® for airborne fungi and the APC M3® for particulate matter. Additionally, fungi from air, bedding and hay were cultured and identified. The median concentration of the total particles in the closed stable was of 2.57 × 107 N/m3, which was twice as high as in the open stable. The median concentration of the total fungi was 7.29 × 103 CFU/m3 in both stables. The dominant fungal genus was Aspergillus. In the open stable the median concentration of Aspergillus sp. was of 5 × 103 CFU/m3 and thus significantly higher (p = 0.001) than in the closed stable. The fungal genera detected in bedding and hay were similar to those in the air of both stables. The findings of this study may contribute to plan and realise horse stables in future, which avoid accumulation of bioaerosols and prevent health hazards to horses and the working team.
{"title":"Comparative investigation of airborne particulate matter and culturable fungi in horse stables","authors":"Doris Haas , Theresa Fritz , Herbert Galler , Juliana Habib , Petra Ofner-Kopeinig , Michael Kropsch , Eduard Zentner , Michael Schalli","doi":"10.1016/j.apr.2025.102683","DOIUrl":"10.1016/j.apr.2025.102683","url":null,"abstract":"<div><div>Today, horses assist us in sports and are partners in our leisure activities. The frequent consumption of roughage and bedding materials, horses and staff are often exposed to high levels of microorganisms in the stable air. The objective of the study was to investigate the emission of particulate matter and culturable fungi in two horse stables of different construction types in urban and rural areas as well as their transmission in the immediate surroundings. The emission measurements were done in an open and a closed stable. To assess the immissions, measurements were conducted at the stables entrance, leeward and windward directions. Samples were carried out using the MAS 100® for airborne fungi and the APC M3® for particulate matter. Additionally, fungi from air, bedding and hay were cultured and identified. The median concentration of the total particles in the closed stable was of 2.57 × 10<sup>7</sup> N/m<sup>3</sup>, which was twice as high as in the open stable. The median concentration of the total fungi was 7.29 × 10<sup>3</sup> CFU/m<sup>3</sup> in both stables. The dominant fungal genus was <em>Aspergillus</em>. In the open stable the median concentration of <em>Aspergillus</em> sp. was of 5 × 10<sup>3</sup> CFU/m<sup>3</sup> and thus significantly higher (p = 0.001) than in the closed stable. The fungal genera detected in bedding and hay were similar to those in the air of both stables. The findings of this study may contribute to plan and realise horse stables in future, which avoid accumulation of bioaerosols and prevent health hazards to horses and the working team.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102683"},"PeriodicalIF":3.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-06DOI: 10.1016/j.apr.2025.102678
J.M. Cordero
Poor air quality in urban areas is an important health risk; therefore, reducing population exposure to pollutants such as PM2.5 is a major concern. Health assessments regarding this pollutant have typically relied on the measurements from urban networks of Air Quality Monitoring Stations (AQMS) to assess population exposure. The methods used for the spatial interpolation of observation often lacks a solid physical basis. Mesoscale air quality models provide high spatiotemporally resolved ground-level concentrations based on urban features, including the distribution of pollution sources; however, they are subject to significant uncertainty. In this work, a novel methodology to produce 1 km2 resolution maps of ground-level PM2.5 concentration for the Municipality of Madrid during 2015 is presented. Toward this end, different data sets including: meteorology, satellite observations of atmospheric optical depth (AOD) from MAIAC, emission data, population, land use, and vegetation land cover have been used. Subsequently, we applied extreme gradient boosting (XGBoost) machine learning algorithms in two steps to first fill gaps in the AOD field and then, estimate ground-level PM2.5 concentration. The predictions of the so-called 2_step_XGBoost algorithm were compared with observations from the all available ground-level PM2.5 concentration observations from the AQMS in Madrid obtaining a determination coefficient (r2) of 0.96, a RMSE of 1.5 μg/m3, and negligible bias. Additionally, we used a 10-fold cross validation to confirm the robustness of the algorithm and the independency of the dataset used for training (r2 of 0.94 ± 0.01, RMSE of 0.40 ± 0.04 and MAE of 0.22 ± 0.02. These results highlight the reliability of this approach for future urban health analysis. In addition, we performed a Feature Importance (FI) analysis that revealed that 2_step_XGBoost identified the planetary boundary layer height (PBLH) as the most influential variable while AOD was found to have relatively low explanatory power, a result that may be contrasted in other case studies.
{"title":"A two-stage algorithm to estimate ground-level PM2.5 concentration levels in Madrid (Spain) from AOD satellite data and surface proxies","authors":"J.M. Cordero","doi":"10.1016/j.apr.2025.102678","DOIUrl":"10.1016/j.apr.2025.102678","url":null,"abstract":"<div><div>Poor air quality in urban areas is an important health risk; therefore, reducing population exposure to pollutants such as PM<sub>2.5</sub> is a major concern. Health assessments regarding this pollutant have typically relied on the measurements from urban networks of Air Quality Monitoring Stations (AQMS) to assess population exposure. The methods used for the spatial interpolation of observation often lacks a solid physical basis. Mesoscale air quality models provide high spatiotemporally resolved ground-level concentrations based on urban features, including the distribution of pollution sources; however, they are subject to significant uncertainty. In this work, a novel methodology to produce 1 km<sup>2</sup> resolution maps of ground-level PM<sub>2.5</sub> concentration for the Municipality of Madrid during 2015 is presented. Toward this end, different data sets including: meteorology, satellite observations of atmospheric optical depth (AOD) from MAIAC, emission data, population, land use, and vegetation land cover have been used. Subsequently, we applied extreme gradient boosting (XGBoost) machine learning algorithms in two steps to first fill gaps in the AOD field and then, estimate ground-level PM<sub>2.5</sub> concentration. The predictions of the so-called 2_step_XGBoost algorithm were compared with observations from the all available ground-level PM<sub>2.5</sub> concentration observations from the AQMS in Madrid obtaining a determination coefficient (r<sup>2</sup>) of 0.96, a RMSE of 1.5 μg/m<sup>3</sup>, and negligible bias. Additionally, we used a 10-fold cross validation to confirm the robustness of the algorithm and the independency of the dataset used for training (r<sup>2</sup> of 0.94 ± 0.01, RMSE of 0.40 ± 0.04 and MAE of 0.22 ± 0.02. These results highlight the reliability of this approach for future urban health analysis. In addition, we performed a Feature Importance (FI) analysis that revealed that 2_step_XGBoost identified the planetary boundary layer height (PBLH) as the most influential variable while AOD was found to have relatively low explanatory power, a result that may be contrasted in other case studies.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102678"},"PeriodicalIF":3.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144826513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-05DOI: 10.1016/j.apr.2025.102679
Kang Ma , Fengman Fang , Yuesheng Lin , Xiuya Xing , Youru Yao , Fei Tong
Ambient fine particulate matter (PM2.5) has been widely associated with pulmonary tuberculosis (PTB). However, less is known about the impact and temporal variations of specific PM2.5 components on PTB. In this study, we employed breakpoint regression and distributed lag nonlinear models (DLNM) to assess the temporal variations in the effects of long−term exposure to PM2.5 and its components, including sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), organic matter (OM), and black carbon (BC) on the incidence of 14,816,329 PTB cases across 31 provinces in mainland China from 2004 to 2018. We also explored sources of heterogeneity through meta-regression. The study found that long−term exposures to PM2.5 components were linked to higher monthly PTB incidence. The cumulative relative risks (CRR and 95 % CI) were highest at 1.379 (95 % CI: 1.331−1.428) per interquartile range (IQR) increase in NH4+, followed by 1.361 (95 % CI: 1.314−1.409) for NO3−, 1.331 (95 % CI: 1.282−1.382) for SO42−, 1.130 (95 % CI: 1.091−1.171) for OM, and 1.126 (95 % CI: 1.084−1.170) for BC. Smear−positive pulmonary tuberculosis (SPPTB) showed greater susceptibility. This association varied significantly across different socioeconomic and healthcare levels. In regions with lower per capita disposable income, urbanization rate, and the number of medical technical personnel per 1000 persons, NO3− and NH4+ were associated with higher PTB incidence rates. Over time, the effect sizes per unit increase in PM2.5 components has significantly diminished, but the impact of NH4+, NO3− and SO42− on PTB remains substantial. This observation might be due to reductions in PM2.5 exposure levels and changes in socioeconomic status factors, such as advancements in the economy and healthcare systems. Our study highlights the temporal variations in the effects of different PM2.5 components on PTB incidence, identifies key harmful components contributing to PTB, and provides scientific evidence for the PTB prevention and control strategies targeting specific PM2.5 sources.
{"title":"Temporal variations in the long–term effects of ambient PM2.5 components on pulmonary tuberculosis incidence in mainland China during 2004–2018","authors":"Kang Ma , Fengman Fang , Yuesheng Lin , Xiuya Xing , Youru Yao , Fei Tong","doi":"10.1016/j.apr.2025.102679","DOIUrl":"10.1016/j.apr.2025.102679","url":null,"abstract":"<div><div>Ambient fine particulate matter (PM<sub>2.5</sub>) has been widely associated with pulmonary tuberculosis (PTB). However, less is known about the impact and temporal variations of specific PM<sub>2.5</sub> components on PTB. In this study, we employed breakpoint regression and distributed lag nonlinear models (DLNM) to assess the temporal variations in the effects of long−term exposure to PM<sub>2.5</sub> and its components, including sulfate (SO<sub>4</sub><sup>2−</sup>), nitrate (NO<sub>3</sub><sup>−</sup>), ammonium (NH<sub>4</sub><sup>+</sup>), organic matter (OM), and black carbon (BC) on the incidence of 14,816,329 PTB cases across 31 provinces in mainland China from 2004 to 2018. We also explored sources of heterogeneity through meta-regression. The study found that long−term exposures to PM<sub>2.5</sub> components were linked to higher monthly PTB incidence. The cumulative relative risks (CRR and 95 % CI) were highest at 1.379 (95 % CI: 1.331−1.428) per interquartile range (IQR) increase in NH<sub>4</sub><sup>+</sup>, followed by 1.361 (95 % CI: 1.314−1.409) for NO<sub>3</sub><sup>−</sup>, 1.331 (95 % CI: 1.282−1.382) for SO<sub>4</sub><sup>2−</sup>, 1.130 (95 % CI: 1.091−1.171) for OM, and 1.126 (95 % CI: 1.084−1.170) for BC. Smear−positive pulmonary tuberculosis (SPPTB) showed greater susceptibility. This association varied significantly across different socioeconomic and healthcare levels. In regions with lower per capita disposable income, urbanization rate, and the number of medical technical personnel per 1000 persons, NO<sub>3</sub><sup>−</sup> and NH<sub>4</sub><sup>+</sup> were associated with higher PTB incidence rates. Over time, the effect sizes per unit increase in PM<sub>2.5</sub> components has significantly diminished, but the impact of NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>−</sup> and SO<sub>4</sub><sup>2−</sup> on PTB remains substantial. This observation might be due to reductions in PM<sub>2.5</sub> exposure levels and changes in socioeconomic status factors, such as advancements in the economy and healthcare systems. Our study highlights the temporal variations in the effects of different PM<sub>2.5</sub> components on PTB incidence, identifies key harmful components contributing to PTB, and provides scientific evidence for the PTB prevention and control strategies targeting specific PM<sub>2.5</sub> sources.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102679"},"PeriodicalIF":3.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-31DOI: 10.1016/j.apr.2025.102677
Annalisa Di Bernardino , Margherita Erriu , Serena Falasca , Anna Maria Siani
This work proposes different methodologies for evaluating the Urban Pollution Island Intensity (UPII), varying the subset of urban air quality stations according to their environmental classification. In-situ observations of particulate matter (PM10 and PM2.5), nitrogen oxides (NO and NO2), and ozone (O3) collected in Rome (Italy) from 2018 to 2023 are examined.
Urban traffic stations, located near roads with medium-high traffic intensity, always recorded higher concentrations than the urban background sites. The selection of both urban traffic and background stations permits the most representative description of UPII and the pollution levels in the metropolitan area. UPII assumes the largest seasonal variation for PM10 and NO, while the daily cycle highlights a bimodal behaviour, with peaks corresponding to rush hours. NO2 peaks are shifted by 1 h compared to NO, while for O3 UPII shows positive values during summertime rush hours. Multi-pollutant air quality indices confirm poor air quality in the city, predominantly during the colder months.
The findings demonstrate that pollution is mainly attributable to NO2, PM10, and O3 in downtown Rome and to PM2.5 and O3 in the countryside. The pollution variations suggest the combined effect of emission-based, meteorological-based, and photochemical-based factors, which must be considered when tailored policies for improving air quality are designed.
{"title":"Exploring different methods to evaluate the Urban Pollution Island intensity based on multi-year observations of aerosol and gases","authors":"Annalisa Di Bernardino , Margherita Erriu , Serena Falasca , Anna Maria Siani","doi":"10.1016/j.apr.2025.102677","DOIUrl":"10.1016/j.apr.2025.102677","url":null,"abstract":"<div><div>This work proposes different methodologies for evaluating the Urban Pollution Island Intensity (UPII), varying the subset of urban air quality stations according to their environmental classification. In-situ observations of particulate matter (PM<sub>10</sub> and PM<sub>2.5</sub>), nitrogen oxides (NO and NO<sub>2</sub>), and ozone (O<sub>3</sub>) collected in Rome (Italy) from 2018 to 2023 are examined.</div><div>Urban traffic stations, located near roads with medium-high traffic intensity, always recorded higher concentrations than the urban background sites. The selection of both urban traffic and background stations permits the most representative description of UPII and the pollution levels in the metropolitan area. UPII assumes the largest seasonal variation for PM<sub>10</sub> and NO, while the daily cycle highlights a bimodal behaviour, with peaks corresponding to rush hours. NO<sub>2</sub> peaks are shifted by 1 h compared to NO, while for O<sub>3</sub> UPII shows positive values during summertime rush hours. Multi-pollutant air quality indices confirm poor air quality in the city, predominantly during the colder months.</div><div>The findings demonstrate that pollution is mainly attributable to NO<sub>2</sub>, PM<sub>10</sub>, and O<sub>3</sub> in downtown Rome and to PM<sub>2.5</sub> and O<sub>3</sub> in the countryside. The pollution variations suggest the combined effect of emission-based, meteorological-based, and photochemical-based factors, which must be considered when tailored policies for improving air quality are designed.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102677"},"PeriodicalIF":3.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-29DOI: 10.1016/j.apr.2025.102675
Mehmet Semih Demirtas , Siddika Songul Yalcin , Mustafa Tusat , Ismail Ozmen , Dilek Basar , Ahmet Salih Calapoglu , Serdar Siyve , Orhan Demirtas , Dilan Altintas Ural
We aimed to investigate the associations between hypospadias and contact to air pollutants and climate parameters during the pre- and post-conceptional periods. This study has been carried out as a multicentre case-control study involving 340 children with hypospadias between the ages of 0–3 years and 357 children in the same age range who applied for circumcision. A total of 7 air pollutant (PM2.5, PM10, NO, NO2, SO2, CO, O3), 5 climate parameters [air temperature, relative humidity (RH), air pressure (AP), wind speed (WS) and precipitation amounts data were obtained. In the multivariate logistic regression analysis, younger maternal age (reference: >35 years) and maternal smoking (reference: no exposure) were significantly associated with a higher likelihood of hypospadias. Conversely, folic acid supplementation intake during pregnancy (reference: no supplementation) and maternal BMI in the range of 25–30 (reference: <25) were associated with a lower likelihood of its occurrence. After adjusting for child-family characteristics, increased interquartile ranges (IQR) of CO, mean temperature, minimum temperature, RH, and AP were positively associated with hypospadias, whereas SO2 levels and diurnal temperature range (DTR) were inversely associated. Comprehensive analysis of individual and environmental factors further highlighted that younger maternal age, smoking exposure, PM10, and air pressure were positively associated with hypospadias, while a greater DTR was inversely associated. Younger maternal age, smoking exposure, and specific air pollutants, such as PM10 and CO, as well as climatic factors like AP, were positively associated with hypospadias. These findings underscore the importance of mitigating environmental exposures and promoting maternal health interventions to reduce the risk of congenital anomalies like hypospadias. Further longitudinal studies are warranted to confirm these associations and explore underlying mechanisms.
{"title":"Association between air pollution and climate parameters exposure during the periconceptional period and hypospadias risk in Turkish offspring: A case-control study","authors":"Mehmet Semih Demirtas , Siddika Songul Yalcin , Mustafa Tusat , Ismail Ozmen , Dilek Basar , Ahmet Salih Calapoglu , Serdar Siyve , Orhan Demirtas , Dilan Altintas Ural","doi":"10.1016/j.apr.2025.102675","DOIUrl":"10.1016/j.apr.2025.102675","url":null,"abstract":"<div><div>We aimed to investigate the associations between hypospadias and contact to air pollutants and climate parameters during the pre- and post-conceptional periods. This study has been carried out as a multicentre case-control study involving 340 children with hypospadias between the ages of 0–3 years and 357 children in the same age range who applied for circumcision. A total of 7 air pollutant (PM<sub>2.5</sub>, PM<sub>10</sub>, NO, NO<sub>2</sub>, SO<sub>2</sub>, CO, O<sub>3</sub>), 5 climate parameters [air temperature, relative humidity (RH), air pressure (AP), wind speed (WS) and precipitation amounts data were obtained. In the multivariate logistic regression analysis, younger maternal age (reference: >35 years) and maternal smoking (reference: no exposure) were significantly associated with a higher likelihood of hypospadias. Conversely, folic acid supplementation intake during pregnancy (reference: no supplementation) and maternal BMI in the range of 25–30 (reference: <25) were associated with a lower likelihood of its occurrence. After adjusting for child-family characteristics, increased interquartile ranges (IQR) of CO, mean temperature, minimum temperature, RH, and AP were positively associated with hypospadias, whereas SO<sub>2</sub> levels and diurnal temperature range (DTR) were inversely associated. Comprehensive analysis of individual and environmental factors further highlighted that younger maternal age, smoking exposure, PM<sub>10</sub>, and air pressure were positively associated with hypospadias, while a greater DTR was inversely associated. Younger maternal age, smoking exposure, and specific air pollutants, such as PM<sub>10</sub> and CO, as well as climatic factors like AP, were positively associated with hypospadias. These findings underscore the importance of mitigating environmental exposures and promoting maternal health interventions to reduce the risk of congenital anomalies like hypospadias. Further longitudinal studies are warranted to confirm these associations and explore underlying mechanisms.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102675"},"PeriodicalIF":3.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144764180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-23DOI: 10.1016/j.apr.2025.102674
Jessika Rodríguez , Héctor Jorquera
This work presents a methodology that combines CALPUFF dispersion model with novel estimates of local concentrations of black carbon and PM2.5 coming from fossil fuel (BCff, PM2.5ff) and wood burning (BCwb, PM2.5wb), to improve local emission estimates without the need to specify boundary conditions. We have applied this methodology in three suburbs of Santiago, Chile — Colina, Melipilla and San José de Maipo (SJM) — during the cold season of 2021. For PM2.5wb, local emission rates were estimated at 74.8 ton year−1 for Colina, 93.3 ton year−1 for Melipilla, and 24.3 ton year−1 for SJM, which correspond to 0.5, 0.4, and 0.3 times the 2012 inventory, respectively. For BCff, the estimated emission rates were 2.2 ton month−1 in Colina, 0.2 ton month−1 in Melipilla, and 0.2 ton month−1 in SJM, which represent 5.2, 0.7, and 2.8 times the respective values at each site according to the 2017 inventory. For both BCwb and BCff, the peaks in local emissions corresponded to times of the day with higher levels of residential combustion and traffic activity. Dispersion modeling of BCff from Zone B (the studied suburbs) to Zone A (Greater Santiago) showed that the highest contributions occurred in July 2021 in the wee hours. Dispersion modeling of PM2.5wb, PM2.5ff, BCwb and BCff from Zone A to Zone B and wind trajectory analysis suggest that non-local contributions reaching the studied suburbs originate not only from Santiago but also from nearby and distant localities. This novel methodology may be applied to any suburban area.
这项工作提出了一种方法，将CALPUFF分散模型与来自化石燃料（BCff, PM2.5ff）和木材燃烧（BCwb, PM2.5wb）的当地黑碳和PM2.5浓度的新估计相结合，以改进当地排放估计，而无需指定边界条件。在2021年的寒冷季节，我们将这种方法应用于智利圣地亚哥的三个郊区——Colina、Melipilla和San jos de Maipo （SJM）。对于PM2.5wb， Colina的当地排放量估计为74.8吨/年，Melipilla为93.3吨/年，SJM为24.3吨/年，分别相当于2012年库存的0.5倍、0.4倍和0.3倍。对于BCff， Colina的估计排放量为2.2吨/月，Melipilla的估计排放量为0.2吨/月，SJM的估计排放量为0.2吨/月，分别是2017年清单中每个站点各自值的5.2倍、0.7倍和2.8倍。对于BCwb和BCff，当地排放峰值对应于一天中住宅燃烧和交通活动水平较高的时间。从B区（研究的郊区）到A区（大圣地亚哥）的BCff分散模型显示，2021年7月凌晨的贡献最大。从A区到B区的PM2.5wb、PM2.5ff、BCwb和BCff弥散模型和风轨迹分析表明，到达研究郊区的非局部贡献不仅来自圣地亚哥，也来自附近和远处的地区。这种新颖的方法可以应用于任何郊区。
{"title":"A methodology for estimation of local black carbon and PM2.5 emissions in suburban zones. Case study of Santiago, Chile","authors":"Jessika Rodríguez , Héctor Jorquera","doi":"10.1016/j.apr.2025.102674","DOIUrl":"10.1016/j.apr.2025.102674","url":null,"abstract":"<div><div>This work presents a methodology that combines CALPUFF dispersion model with novel estimates of local concentrations of black carbon and PM<sub>2.5</sub> coming from fossil fuel (BC<sub>ff</sub>, PM<sub>2.5ff</sub>) and wood burning (BC<sub>wb</sub>, PM<sub>2.5wb</sub>), to improve local emission estimates without the need to specify boundary conditions. We have applied this methodology in three suburbs of Santiago, Chile — Colina, Melipilla and San José de Maipo (SJM) — during the cold season of 2021. For PM<sub>2.5wb</sub>, local emission rates were estimated at 74.8 ton year<sup>−1</sup> for Colina, 93.3 ton year<sup>−1</sup> for Melipilla, and 24.3 ton year<sup>−1</sup> for SJM, which correspond to 0.5, 0.4, and 0.3 times the 2012 inventory, respectively. For BC<sub>ff</sub>, the estimated emission rates were 2.2 ton month<sup>−1</sup> in Colina, 0.2 ton month<sup>−1</sup> in Melipilla, and 0.2 ton month<sup>−1</sup> in SJM, which represent 5.2, 0.7, and 2.8 times the respective values at each site according to the 2017 inventory. For both BC<sub>wb</sub> and BC<sub>ff</sub>, the peaks in local emissions corresponded to times of the day with higher levels of residential combustion and traffic activity. Dispersion modeling of BC<sub>ff</sub> from Zone B (the studied suburbs) to Zone A (Greater Santiago) showed that the highest contributions occurred in July 2021 in the wee hours. Dispersion modeling of PM<sub>2.5wb</sub>, PM<sub>2.5ff</sub>, BC<sub>wb</sub> and BC<sub>ff</sub> from Zone A to Zone B and wind trajectory analysis suggest that non-local contributions reaching the studied suburbs originate not only from Santiago but also from nearby and distant localities. This novel methodology may be applied to any suburban area.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102674"},"PeriodicalIF":3.5,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-23DOI: 10.1016/j.apr.2025.102670
Giovanna Mazzi , Elena Barbaro , Matteo Feltracco , Marco Roman , Emma Camarotto , Emanuela Canò , Giada Furlan , Andrea Gambaro
Illicit drugs (IDs) are substances whose production, consumption, and health impact are increasing globally. While their presence in water and soil has been studied, airborne concentrations remain largely unexplored. Airborne IDs could serve as markers of consumption patterns and potentially help identify local drug distribution zones. Few analytical methods have been developed, but preanalytical treatments are often time-consuming. To address this, we developed and validated a reliable, fast analytical method based on HPLC-MS/MS for cocaine, amphetamine, and heroin – three largely consumed IDs. Low Method Detection and Quantification Limits were achieved, with cocaine reaching the lowest reported in the literature (0.02 and 0.08 pg m−3, respectively).
Our methodology was applied to nearly 180 p.m.10 samples collected during December 2023 across thirteen cities in northern Italy. Cocaine was always detected and quantified, whereas amphetamine and heroin were below the detection limit. The highest cocaine concentrations were recorded in Milan (229 pg m−3), Collegno (195 pg m−3), and Treviso (111 pg m−3). A notable increase in cocaine levels over the month suggests a link with holidays, tourism, and the Christmas Depression phenomenon. In some cities, concentration spikes coincided with traditional festivities. Statistical analyses were conducted to explore correlations between cocaine, PM10, inorganic major ions, and atmospheric variables. Negative correlation was found between cocaine concentration and wind in six cities, suggesting multiple local sources. Local available healthcare and drug seizure data were also considered, and an interesting correlation was found between cocaine levels and people taken into care by private services.
在全球范围内，非法药物的生产、消费和对健康的影响都在增加。虽然已经研究了它们在水和土壤中的存在，但空气中的浓度仍未得到很大程度的探索。通过空气传播的身份证可以作为消费模式的标志，并可能有助于识别当地的毒品分销区域。很少有分析方法被开发出来，但分析前处理通常是耗时的。为了解决这个问题，我们开发并验证了一种基于HPLC-MS/MS的可靠、快速的可卡因、安非他明和海洛因分析方法。方法检测和定量限较低，可卡因达到文献报道的最低（分别为0.02和0.08 pg m - 3）。我们的方法应用于2023年12月在意大利北部13个城市收集的近180个下午10点样本。可卡因总能被检出并定量，而安非他明和海洛因则低于检出限。可卡因浓度最高的地区是米兰（229 pg m - 3）、科利戈诺（195 pg m - 3）和特雷维索（111 pg m - 3）。一个月内可卡因含量的显著增加表明，这与假日、旅游和圣诞大萧条现象有关。在一些城市，人口集中高峰与传统节日同时发生。统计分析探讨了可卡因、PM10、无机主要离子和大气变量之间的相关性。6个城市的可卡因浓度与风力呈负相关，表明有多个地方来源。还考虑了当地现有的保健和毒品缉获数据，发现可卡因水平与接受私人服务的人之间存在有趣的相关性。
{"title":"Investigating illicit drugs in PM10 from Northern Italy using a rapid HPLC-MS/MS method","authors":"Giovanna Mazzi , Elena Barbaro , Matteo Feltracco , Marco Roman , Emma Camarotto , Emanuela Canò , Giada Furlan , Andrea Gambaro","doi":"10.1016/j.apr.2025.102670","DOIUrl":"10.1016/j.apr.2025.102670","url":null,"abstract":"<div><div>Illicit drugs (IDs) are substances whose production, consumption, and health impact are increasing globally. While their presence in water and soil has been studied, airborne concentrations remain largely unexplored. Airborne IDs could serve as markers of consumption patterns and potentially help identify local drug distribution zones. Few analytical methods have been developed, but preanalytical treatments are often time-consuming. To address this, we developed and validated a reliable, fast analytical method based on HPLC-MS/MS for cocaine, amphetamine, and heroin – three largely consumed IDs. Low Method Detection and Quantification Limits were achieved, with cocaine reaching the lowest reported in the literature (0.02 and 0.08 pg m<sup>−3</sup>, respectively).</div><div>Our methodology was applied to nearly 180 p.m.<sub>10</sub> samples collected during December 2023 across thirteen cities in northern Italy. Cocaine was always detected and quantified, whereas amphetamine and heroin were below the detection limit. The highest cocaine concentrations were recorded in Milan (229 pg m<sup>−3</sup>), Collegno (195 pg m<sup>−3</sup>), and Treviso (111 pg m<sup>−3</sup>). A notable increase in cocaine levels over the month suggests a link with holidays, tourism, and the <em>Christmas Depression</em> phenomenon. In some cities, concentration spikes coincided with traditional festivities. Statistical analyses were conducted to explore correlations between cocaine, PM<sub>10</sub>, inorganic major ions, and atmospheric variables. Negative correlation was found between cocaine concentration and wind in six cities, suggesting multiple local sources. Local available healthcare and drug seizure data were also considered, and an interesting correlation was found between cocaine levels and people taken into care by private services.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102670"},"PeriodicalIF":3.5,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-23DOI: 10.1016/j.apr.2025.102672
Myeong-Gyun Kim , Se-Young Kim , Kwangyul Lee , Pilho Kim , Hyoseon Kim , Hyo-Jong Song
PM2.5 is emitted and formed in the atmosphere through various factors, posing significant health risks to humans. Therefore, accurately estimating PM2.5 concentrations and analyzing the contributions of individual factors are crucial. A Deep Neural Network (DNN) model was developed for PM2.5 estimation in the Seoul Metropolitan Area in South Korea, while some machine learning models—Random Forest and Extreme Gradient Boosting—were also built for performance comparison. Among these, the DNN model demonstrated the best performance, with an R2 of 0.95, MSE of 12.14, and MAE of 2.6. Based on this, Explainable Artificial Intelligence (XAI) techniques, including Vanilla Gradient and Shapley Additive Explanation (SHAP), were applied to interpret the PM2.5 estimation model and analyze the contribution of each factor. The contribution analysis for the Seoul Metropolitan Area revealed that NO3− and NH4+ had the highest contributions to PM2.5 formation, indicating that secondary formation mechanisms play a dominant role. Furthermore, at high concentrations, the contributions of NO3−, NH4+, and SO42− were the highest, and the contributions of metal components and PM10 were higher than the average. In particular, it was observed that NH4+ and K showed a positive correlation with PM2.5 formation. Future research will focus on refining the model through clustering-based approaches and other enhancements, aiming to deepen the understanding of PM2.5 formation patterns and provide meaningful insights for policymaking.
{"title":"Development and interpretation of PM2.5 estimation model for the Seoul Metropolitan Area using machine learning and explainable AI","authors":"Myeong-Gyun Kim , Se-Young Kim , Kwangyul Lee , Pilho Kim , Hyoseon Kim , Hyo-Jong Song","doi":"10.1016/j.apr.2025.102672","DOIUrl":"10.1016/j.apr.2025.102672","url":null,"abstract":"<div><div>PM<sub>2.5</sub> is emitted and formed in the atmosphere through various factors, posing significant health risks to humans. Therefore, accurately estimating PM<sub>2.5</sub> concentrations and analyzing the contributions of individual factors are crucial. A Deep Neural Network (DNN) model was developed for PM<sub>2.5</sub> estimation in the Seoul Metropolitan Area in South Korea, while some machine learning models—Random Forest and Extreme Gradient Boosting—were also built for performance comparison. Among these, the DNN model demonstrated the best performance, with an R<sup>2</sup> of 0.95, MSE of 12.14, and MAE of 2.6. Based on this, Explainable Artificial Intelligence (XAI) techniques, including Vanilla Gradient and Shapley Additive Explanation (SHAP), were applied to interpret the PM<sub>2.5</sub> estimation model and analyze the contribution of each factor. The contribution analysis for the Seoul Metropolitan Area revealed that NO<sub>3</sub><sup>−</sup> and NH<sub>4</sub><sup>+</sup> had the highest contributions to PM<sub>2.5</sub> formation, indicating that secondary formation mechanisms play a dominant role. Furthermore, at high concentrations, the contributions of NO<sub>3</sub><sup>−</sup>, NH<sub>4</sub><sup>+</sup>, and SO<sub>4</sub><sup>2−</sup> were the highest, and the contributions of metal components and PM<sub>10</sub> were higher than the average. In particular, it was observed that NH<sub>4</sub><sup>+</sup> and K showed a positive correlation with PM<sub>2.5</sub> formation. Future research will focus on refining the model through clustering-based approaches and other enhancements, aiming to deepen the understanding of PM<sub>2.5</sub> formation patterns and provide meaningful insights for policymaking.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 11","pages":"Article 102672"},"PeriodicalIF":3.9,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Indoor air quality is crucial for the well-being of residents, as people spend 80 % of their time indoors. This study investigated the impact of festival activities on household indoor air quality in megacity Delhi. The study included different phases and activities during the festival and normal conditions and analyzed the impact of different emission sources. During the festival period, the indoor ultrafine particle concentration was found to be 6.7 × 104 #cm−3, which was the highest observed indoor concentration throughout the study. The particle number concentration of the nanoparticles in indoor environment ranged from 104 to 105 #cm−3. Ultrafine size range particles contributed up to ∼85 % to the total particle numbers. Fireworks contributed to higher particle numbers in indoors, followed by cooking, dusting, and worshiping. The size distribution pattern of the particles emitted during cooking activities and fireworks was found to be different. Particle size range 10–30 nm contributed ∼31 % to total particle numbers on fireworks day, whereas on a normal day, it contributed only ∼13 %. During normal day, 100–1000 nm size particles contributed ∼50 % to total particle numbers. The diurnal pattern of the indoor environment was different from the outdoor. The outdoor fireworks activities also influenced indoor pollutants with respect to trace metals, which makes the indoor air quality more toxic, and affects the occupants' health.
{"title":"Indoor ultrafine particle concentrations and their size distributions during festival-associated emissions in urban region","authors":"Monika Sharma , Mukesh Khare , Rajeev Kumar Mishra","doi":"10.1016/j.apr.2025.102671","DOIUrl":"10.1016/j.apr.2025.102671","url":null,"abstract":"<div><div>Indoor air quality is crucial for the well-being of residents, as people spend 80 % of their time indoors. This study investigated the impact of festival activities on household indoor air quality in megacity Delhi. The study included different phases and activities during the festival and normal conditions and analyzed the impact of different emission sources. During the festival period, the indoor ultrafine particle concentration was found to be 6.7 × 10<sup>4</sup> #cm<sup>−3</sup>, which was the highest observed indoor concentration throughout the study. The particle number concentration of the nanoparticles in indoor environment ranged from 10<sup>4</sup> to 10<sup>5</sup> #cm<sup>−3</sup>. Ultrafine size range particles contributed up to ∼85 % to the total particle numbers. Fireworks contributed to higher particle numbers in indoors, followed by cooking, dusting, and worshiping. The size distribution pattern of the particles emitted during cooking activities and fireworks was found to be different. Particle size range 10–30 nm contributed ∼31 % to total particle numbers on fireworks day, whereas on a normal day, it contributed only ∼13 %. During normal day, 100–1000 nm size particles contributed ∼50 % to total particle numbers. The diurnal pattern of the indoor environment was different from the outdoor. The outdoor fireworks activities also influenced indoor pollutants with respect to trace metals, which makes the indoor air quality more toxic, and affects the occupants' health.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102671"},"PeriodicalIF":3.5,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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